The advent of better technologies in the field of visual signal capture and processing has fueled a paradigm shift in todays' multimedia communication systems. As a result, the notion of network-centric quality of service (QoS) in multimedia systems is being extended by relying on the concept of quality of experience (QoE). In this quest of increasing the immersive video experience and the overall QoE of the end user, newer technologies such as 3D, ultra-high definition (UHD) and, more recently, high dynamic Range (HDR) imaging have gained prominence within the multimedia signal processing community. HDR in particular has attracted attention since it in a way revisits the way we capture and display natural scenes. This is motivated by the fact that natural scenes often exhibit large ranges of illumination values. However, such high luminance values often exceed the capabilities of the traditional low dynamic range (LDR) capturing and display devices. Consequently, it is not possible to properly expose the dark and the bright areas simultaneously in one image or one video during capture. This may lead to over-exposure (saturated pixels that are fully white) and/or under-exposure (very dark or noisy pixels as sensor's response falls below its noise threshold). In both cases, visual information is either lost or altered. HDR imaging focuses on minimizing such losses and therefore aims at improving the quality of the displayed pixels by incorporating higher contrast and luminance.
As a result, HDR imaging has attracted attention from both academia and industry, and there has been interest and effort to develop tools/algorithms for HDR video processing. For instance, there have been recent efforts within the Moving Picture Experts Group (MPEG) for extending High Efficiency Video Coding (HEVC) to HDR. Likewise, the JPEG has announced extensions that will feature the original JPEG standard with support for HDR image compression. Despite of some work on evaluating quality of HDR images and video sequences, there is overall lack of such efforts to quantify and measure the impact of such tools on HDR video quality using both subjective and objective approaches.
It is therefore important to develop objective methods for HDR video quality measurement and benchmark their performance against subjective ground truth.
With regards to visual quality measurement, both subjective and objective approaches can be used. The former involves the use of human subjects to judge and rate the quality of the test stimuli. With appropriate laboratory conditions and a sufficiently large subject panel, it remains the most accurate method. The latter quality assessment method employs a computational model to provide estimates of the subjective video quality. While such objective models may not mimic subjective opinions accurately in a general scenario, they can be reasonably effective in specific conditions/applications. Hence, they can be an important tool towards automating the testing and standardization of HDR video processing algorithms such as HDR video compression, post-processing, inverse video tone mapping, etc., especially when subjective tests may not be feasible.
Therefore, there is a need for a tool for determining automatically a visual quality index of a HDR video sequence that has undergone distortions due to image processing operations such as HDR video compression/decompression, post-processing, inverse video tone mapping.